We are interested in modeling the semantics of words and complete
news articles to obtain suitable representations for predicting distress.
At the word level, distributional semantics exploits the linguistic
property that words of similar meaning tend to occur in similar contexts
[1]}. Modeling of word contexts yields
distributed representations of word semantics as vectors, which allow
measuring of semantic similarities and detecting analogies without
supervision, given substantial amounts of text [2]}, [3]}, [4]}.
These word vectors provide a continuous semantic space embedding where
the symbolic input of words can be compared quantitatively,
thus supporting both the prediction task of this paper and a multitude
of other natural language processing tasks (e.g., tagging, parsing
and relation extraction [5]}).
